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wafer-inspection-yield

fab defect inspection, metrology and yield analytics: die-to-die and die-to-database defect detection, wafer-map spatial signatures, inline SPC, defect review and classification

KLA, Applied SEMVision, PDF Solutions Exensio

SystemTechniques · 18Primitives · 62 wafer-inspection-yield Active learningmachine-learning · medium: Choosing which unlabeled examples to label next: uncertainty, margin, diversity and committee samplingDensity-based clustering (DBSCAN)machine-learning · medium: Group points that are packed densely, label the rest as noise: no k, arbitrary shapes, two parameters (eps, minPts), cost set by the neighbour index.›Geometric pattern matchingmachine-learning · medium: Find a model contour in an image under rotation and scale using gradient-orientation features and a pyramid search with a match scoreGradient-boosted decision trees (GB…machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.›Image segmentationmachine-learning · medium: Pixel/instance segmentation networks and promptable segmentation (Mask R-CNN, SAM-style)Image alignment registrationmedia · medium: Align frames via feature matching or pyramidal optical flow for burst merging and stitchingImage denoisingmedia · medium: Bilateral, non-local means and temporal noise reduction for sensor imagesk-means clusteringmachine-learning · low: Partition points into k clusters by alternating nearest-centroid assignment and centroid averaging; k-means++ seeding makes it dependable.›k-d treegeometry · low: A binary tree that splits k-dimensional points by one coordinate per level, giving fast nearest-neighbour and box queries on static low-dimensional data.›Convolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Robust statisticsstatistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.›Isolation forestmachine-learning · low: Score each record by how few random axis-aligned splits it takes to isolate it in a small forest of random trees; anomalies are few and different, so they isolate fast.›Subpixel edge measurementmachine-learning · low: Locate edges to a fraction of a pixel by interpolating the gradient peak along caliper projections, then fit lines or circlesWeight quantizationml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.›Secs gem equipment protocolnetworking · low: SECS-II message structures over HSMS or SECS-I with GEM state models for semiconductor tool control, events and recipesFast Fourier transformnumerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.›Negative binomial yield modelstatistics · low: Yield models with defect clustering (negative binomial, Murphy) from defect density and critical areaColumnar file formatstorage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)Calibrationmachine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.›Embeddingsmachine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.›Union-findgraphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.›Quantile sketchesdata-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histogramsLoss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›+ 11 more techniques+ 46 more primitives

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